Himasree
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CodeEmbed-checkpoints

Feature Extraction

information-retrieval

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 ## Overview
-CodeEmbed is a code retrieval research project developed by Himasree Panku.
 The project explores neural code retrieval using transformer-based bi-encoder architectures, dense semantic retrieval, BM25 lexical retrieval, and hybrid retrieval strategies.
 This repository contains trained model checkpoints from the CodeEmbed experiments, including baseline models, dual-encoder experiments, ablation studies, hybrid retrieval experiments, and Phase 7 evaluation models.
-## Author
-**Himasree Panku**
 The CodeEmbed training experiments, retrieval system implementation, experiment configurations, evaluation workflow, and associated checkpoints were developed as part of this project.

 ## Overview
+CodeEmbed
 The project explores neural code retrieval using transformer-based bi-encoder architectures, dense semantic retrieval, BM25 lexical retrieval, and hybrid retrieval strategies.
 This repository contains trained model checkpoints from the CodeEmbed experiments, including baseline models, dual-encoder experiments, ablation studies, hybrid retrieval experiments, and Phase 7 evaluation models.
 The CodeEmbed training experiments, retrieval system implementation, experiment configurations, evaluation workflow, and associated checkpoints were developed as part of this project.